This course is part of the Machine Learning for Trading Specialization

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stars
777 ratings

Jack Farmer

40,948 already enrolled

Offered By

Google Cloud

New York Institute of Finance

Machine Learning for Trading SpecializationGoogle Cloud

About this Course

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In this course, you’ll learn about the fundamentals of trading, including the concept of trend, returns, stop-loss, and volatility. You will learn how to identify the profit source and structure of basic quantitative trading strategies. This course will help you gauge how well the model generalizes its learning, explain the differences between regression and forecasting, and identify the steps needed to create development and implementation backtesters. By the end of the course, you will be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks. 
To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 1 of 3 in the

Machine Learning for Trading Specialization

Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 9 hours to complete

English

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What you will learn

Understand the fundamentals of trading, including the concepts of trend, returns, stop-loss, and volatility.
Define quantitative trading and the main types of quantitative trading strategies.
Understand the basic steps in exchange arbitrage, statistical arbitrage, and index arbitrage.
Understand the application of machine learning to financial use cases.

Skills you will gain

Finance
Trading
Investment
Machine Learning applied to Finance

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 3 in the

Machine Learning for Trading Specialization

Intermediate Level

Familiarization with basic concepts in Machine Learning and Financial Markets; advanced competency in Python Programming.

Approx. 9 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.41/5 (164 Ratings)

Jack Farmer

Curriculum Director

New York Institute of Finance

83,791 Learners

13 Courses

Offered by

Google Cloud

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

New York Institute of Finance

The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks.

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Syllabus - What you will learn from this course

Content Rating87%(3,610 ratings)

Week1

Week 1

4 hours to complete

Introduction to Trading with Machine Learning on Google Cloud

4 hours to complete

26 videos (Total 131 min), 3 readings, 5 quizzes

26 videos

Class Overview - Who these courses are for1mCourse Overview Introduction to Trading with Machine Learning on Google Cloud5mWhat is AI and ML ? What is the difference between AI and ML?58sApplications of ML in the Real World1mWhat is ML?3mGame: The importance of good data4mBrief History of ML in Quantitative Finance11mWhy Google?1mWhy Google Cloud Platform?2mWhat are AI Platform Notebooks1mUsing Notebooks1mBenefits of AI Platform Notebooks2mWhat do we want to model? Let's start simple5mDemo: Building a model with BigQuery ML25mHow to use Qwiklabs for your Labs3mLab Intro: Building a Regression Model37sLab Walkthrough: Building a Regression Model9mTrading vs Investing6mThe Quant Universe2mQuant Strategies7mQuant Trading Advantages and Disadvantages4mExchange and Statistical Arbitrage8mIndex Arbitrage2mStatistical Arbitrage Opportunities and Challenges5mIntroduction to Backtesting5mBacktesting Design6m

3 readings

Supervised Learning and Regression10mWelcome to Introduction to Trading, Machine Learning and GCP10mCase Study: Capital Markets in the Cloud10m

4 practice exercises

Python Skills Assessment QuizAI and Machine Learning5mGoogle CloudTrading Concepts Review15m

Week2

Week 2

3 hours to complete

Supervised Learning with BigQuery ML

3 hours to complete

6 videos (Total 29 min), 1 reading, 3 quizzes

Week3

Week 3

2 hours to complete

Time Series and ARIMA Modeling

2 hours to complete

11 videos (Total 52 min)

11 videos

What is a time series?7mAR - Auto Regressive7mMA - Moving Average2mThe Complete ARIMA Model4mARIMA compared to linear regression7mHow can you get a variety of models from just a single series?1mHow to choose ARIMA parameters for your trading model4mTime Series Terminology: Auto Correlation4mSensitivity of Trading Strategy4mLab Intro: Forecasting Stock Prices Using ARIMA32sLab Walkthrough: Forecasting Stock Prices using ARIMA7m

1 practice exercise

Time Series

Week4

Week 4

1 hour to complete

Introduction to Neural Networks and Deep Learning

1 hour to complete

5 videos (Total 29 min), 1 reading, 2 quizzes

Reviews

4.0

206 reviews

5 stars
44.61%
4 stars
30%
3 stars
14.35%
2 stars
4.35%
1 star
6.66%

TOP REVIEWS FROM INTRODUCTION TO TRADING, MACHINE LEARNING & GCP

by AAJan 12, 2020

Good course that gives a lot of breadth as an introduction to machine learning in finance. Well put together

by BPApr 3, 2020

Not as much coding as I would have wanted, or atleast exposure to code. Very solid historical context though.

by BAMar 15, 2020

Very good course us introduction to Trading, ML models for trading, ML, Neural networks concept and approaches, Google cloud platform.

by MLJul 13, 2021

Even are more basic knowledge of Trading and ML, still with specific data relative Trading and finance, Great!

View all reviews

About the Machine Learning for Trading Specialization

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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